Open Intent Discovery through Unsupervised Semantic Clustering and Dependency Parsing

• URL: http://arxiv.org/abs/2104.12114v1
• Date: Sun, 25 Apr 2021 09:36:23 GMT
• Title: Open Intent Discovery through Unsupervised Semantic Clustering and Dependency Parsing
• Authors: Pengfei Liu, Youzhang Ning, King Keung Wu, Kun Li and Helen Meng
• Abstract summary: This paper proposes an unsupervised two-stage approach to discover intents and generate intent labels automatically from a collection of unlabeled utterances. We empirically show that the proposed unsupervised approach can generate meaningful intent labels automatically and achieves high precision and recall in utterance clustering and intent discovery.
• Score: 44.99113692679489
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Intent understanding plays an important role in dialog systems, and is typically formulated as a supervised classification problem. However, it is challenging and time-consuming to design the intent labels manually to support a new domain. This paper proposes an unsupervised two-stage approach to discover intents and generate meaningful intent labels automatically from a collection of unlabeled utterances. In the first stage, we aim to generate a set of semantically coherent clusters where the utterances within each cluster convey the same intent. We obtain the utterance representation from various pre-trained sentence embeddings and present a metric of balanced score to determine the optimal number of clusters in K-means clustering. In the second stage, the objective is to generate an intent label automatically for each cluster. We extract the ACTION-OBJECT pair from each utterance using a dependency parser and take the most frequent pair within each cluster, e.g., book-restaurant, as the generated cluster label. We empirically show that the proposed unsupervised approach can generate meaningful intent labels automatically and achieves high precision and recall in utterance clustering and intent discovery.

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